Overview

Dataset statistics

Number of variables28
Number of observations23895
Missing cells129792
Missing cells (%)19.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.8 MiB
Average record size in memory254.1 B

Variable types

Numeric21
DateTime1
Categorical6

Alerts

shoton_away is highly overall correlated with shoton_home and 6 other fieldsHigh correlation
shoton_home is highly overall correlated with shoton_away and 6 other fieldsHigh correlation
shotoff_away is highly overall correlated with shoton_away and 6 other fieldsHigh correlation
shotoff_home is highly overall correlated with shoton_away and 6 other fieldsHigh correlation
foulcommit_home is highly overall correlated with shoton_away and 6 other fieldsHigh correlation
foulcommit_away is highly overall correlated with shoton_away and 6 other fieldsHigh correlation
cross_home is highly overall correlated with shoton_away and 6 other fieldsHigh correlation
cross_away is highly overall correlated with shoton_away and 6 other fieldsHigh correlation
possession_home is highly overall correlated with possession_awayHigh correlation
possession_away is highly overall correlated with possession_homeHigh correlation
homewin is highly overall correlated with winning_team and 2 other fieldsHigh correlation
winning_team is highly overall correlated with homewin and 2 other fieldsHigh correlation
home_team_result is highly overall correlated with homewin and 2 other fieldsHigh correlation
away_team_result is highly overall correlated with homewin and 2 other fieldsHigh correlation
shoton_away has 9740 (40.8%) missing valuesMissing
shoton_home has 9740 (40.8%) missing valuesMissing
shotoff_away has 9740 (40.8%) missing valuesMissing
shotoff_home has 9740 (40.8%) missing valuesMissing
foulcommit_home has 9740 (40.8%) missing valuesMissing
foulcommit_away has 9740 (40.8%) missing valuesMissing
card_home has 9740 (40.8%) missing valuesMissing
card_away has 9740 (40.8%) missing valuesMissing
cross_home has 9740 (40.8%) missing valuesMissing
cross_away has 9740 (40.8%) missing valuesMissing
possession_home has 15496 (64.9%) missing valuesMissing
possession_away has 15496 (64.9%) missing valuesMissing
home_1st_last_result has 698 (2.9%) missing valuesMissing
away_1st_last_result has 702 (2.9%) missing valuesMissing
home_team_goal has 5391 (22.6%) zerosZeros
away_team_goal has 7898 (33.1%) zerosZeros
shoton_away has 5893 (24.7%) zerosZeros
shoton_home has 5784 (24.2%) zerosZeros
shotoff_away has 5859 (24.5%) zerosZeros
shotoff_home has 5760 (24.1%) zerosZeros
foulcommit_home has 5712 (23.9%) zerosZeros
foulcommit_away has 5711 (23.9%) zerosZeros
card_home has 2010 (8.4%) zerosZeros
card_away has 1295 (5.4%) zerosZeros
cross_home has 5713 (23.9%) zerosZeros
cross_away has 5716 (23.9%) zerosZeros

Reproduction

Analysis started2023-01-31 03:51:51.608654
Analysis finished2023-01-31 03:52:40.069043
Duration48.46 seconds
Software versionpandas-profiling v3.6.5
Download configurationconfig.json

Variables

country_id
Real number (ℝ)

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11508.694
Minimum1
Maximum24558
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size373.4 KiB
2023-01-31T03:52:40.120216image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14769
median10257
Q319694
95-th percentile24558
Maximum24558
Range24557
Interquartile range (IQR)14925

Descriptive statistics

Standard deviation7646.495
Coefficient of variation (CV)0.66441034
Kurtosis-1.2915254
Mean11508.694
Median Absolute Deviation (MAD)7385
Skewness0.11192298
Sum2.7500023 × 108
Variance58468885
MonotonicityIncreasing
2023-01-31T03:52:40.182516image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
1729 3040
12.7%
4769 3020
12.6%
10257 3017
12.6%
21518 3017
12.6%
7809 2424
10.1%
13274 2218
9.3%
17642 1868
7.8%
19694 1797
7.5%
1 1501
6.3%
24558 1293
5.4%
ValueCountFrequency (%)
1 1501
6.3%
1729 3040
12.7%
4769 3020
12.6%
7809 2424
10.1%
10257 3017
12.6%
13274 2218
9.3%
15722 700
 
2.9%
17642 1868
7.8%
19694 1797
7.5%
21518 3017
12.6%
ValueCountFrequency (%)
24558 1293
5.4%
21518 3017
12.6%
19694 1797
7.5%
17642 1868
7.8%
15722 700
 
2.9%
13274 2218
9.3%
10257 3017
12.6%
7809 2424
10.1%
4769 3020
12.6%
1729 3040
12.7%

season
Real number (ℝ)

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2011.6804
Minimum2008
Maximum2015
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size373.4 KiB
2023-01-31T03:52:40.243978image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum2008
5-th percentile2008
Q12010
median2012
Q32014
95-th percentile2015
Maximum2015
Range7
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.2657476
Coefficient of variation (CV)0.001126296
Kurtosis-1.2294106
Mean2011.6804
Median Absolute Deviation (MAD)2
Skewness-0.06996398
Sum48069102
Variance5.133612
MonotonicityNot monotonic
2023-01-31T03:52:40.301187image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
2014 3315
13.9%
2015 3312
13.9%
2012 3007
12.6%
2010 3005
12.6%
2013 2994
12.5%
2011 2975
12.5%
2009 2935
12.3%
2008 2352
9.8%
ValueCountFrequency (%)
2008 2352
9.8%
2009 2935
12.3%
2010 3005
12.6%
2011 2975
12.5%
2012 3007
12.6%
2013 2994
12.5%
2014 3315
13.9%
2015 3312
13.9%
ValueCountFrequency (%)
2015 3312
13.9%
2014 3315
13.9%
2013 2994
12.5%
2012 3007
12.6%
2011 2975
12.5%
2010 3005
12.6%
2009 2935
12.3%
2008 2352
9.8%

stage
Real number (ℝ)

Distinct38
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.548483
Minimum1
Maximum38
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size373.4 KiB
2023-01-31T03:52:40.378186image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q110
median19
Q327
95-th percentile35
Maximum38
Range37
Interquartile range (IQR)17

Descriptive statistics

Standard deviation10.488499
Coefficient of variation (CV)0.56546396
Kurtosis-1.1406785
Mean18.548483
Median Absolute Deviation (MAD)9
Skewness0.040796542
Sum443216
Variance110.0086
MonotonicityNot monotonic
2023-01-31T03:52:40.457393image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
28 688
 
2.9%
25 687
 
2.9%
29 686
 
2.9%
30 685
 
2.9%
26 682
 
2.9%
27 681
 
2.8%
24 680
 
2.8%
23 672
 
2.8%
22 671
 
2.8%
4 668
 
2.8%
Other values (28) 17095
71.5%
ValueCountFrequency (%)
1 663
2.8%
2 661
2.8%
3 664
2.8%
4 668
2.8%
5 665
2.8%
6 664
2.8%
7 662
2.8%
8 663
2.8%
9 662
2.8%
10 663
2.8%
ValueCountFrequency (%)
38 360
1.5%
37 365
1.5%
36 404
1.7%
35 405
1.7%
34 570
2.4%
33 573
2.4%
32 572
2.4%
31 571
2.4%
30 685
2.9%
29 686
2.9%

date
Date

Distinct1634
Distinct (%)6.8%
Missing0
Missing (%)0.0%
Memory size373.4 KiB
Minimum2008-08-09 00:00:00
Maximum2016-05-25 00:00:00
2023-01-31T03:52:40.545332image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:40.634022image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

home_team_api_id
Real number (ℝ)

Distinct294
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10174.722
Minimum1601
Maximum274581
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size373.4 KiB
2023-01-31T03:52:40.717599image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1601
5-th percentile7842
Q18528
median9747
Q39925
95-th percentile10251
Maximum274581
Range272980
Interquartile range (IQR)1397

Descriptive statistics

Standard deviation14589.463
Coefficient of variation (CV)1.4338929
Kurtosis209.18864
Mean10174.722
Median Absolute Deviation (MAD)1025
Skewness14.031873
Sum2.4312499 × 108
Variance2.1285242 × 108
MonotonicityNot monotonic
2023-01-31T03:52:40.794731image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8689 152
 
0.6%
8472 152
 
0.6%
9827 152
 
0.6%
8302 152
 
0.6%
9748 152
 
0.6%
8533 152
 
0.6%
8668 152
 
0.6%
8586 152
 
0.6%
9825 152
 
0.6%
10260 152
 
0.6%
Other values (284) 22375
93.6%
ValueCountFrequency (%)
1601 45
0.2%
1773 45
0.2%
1957 45
0.2%
2033 74
0.3%
2182 45
0.2%
2186 44
0.2%
4049 5
 
< 0.1%
4064 15
 
0.1%
4087 76
0.3%
4170 19
 
0.1%
ValueCountFrequency (%)
274581 30
 
0.1%
208931 19
 
0.1%
188163 17
 
0.1%
177361 15
 
0.1%
158085 48
 
0.2%
108893 17
 
0.1%
10281 76
0.3%
10278 19
 
0.1%
10269 135
0.6%
10268 38
 
0.2%

away_team_api_id
Real number (ℝ)

Distinct294
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10185.285
Minimum1601
Maximum274581
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size373.4 KiB
2023-01-31T03:52:40.880748image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1601
5-th percentile7842
Q18528
median9747
Q39925
95-th percentile10251
Maximum274581
Range272980
Interquartile range (IQR)1397

Descriptive statistics

Standard deviation14616.995
Coefficient of variation (CV)1.4351091
Kurtosis207.72821
Mean10185.285
Median Absolute Deviation (MAD)1025
Skewness13.976375
Sum2.4337738 × 108
Variance2.1365654 × 108
MonotonicityNot monotonic
2023-01-31T03:52:40.959201image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8634 152
 
0.6%
9925 152
 
0.6%
10194 152
 
0.6%
8586 152
 
0.6%
8472 152
 
0.6%
8456 152
 
0.6%
8564 152
 
0.6%
8650 152
 
0.6%
10252 152
 
0.6%
9825 152
 
0.6%
Other values (284) 22375
93.6%
ValueCountFrequency (%)
1601 45
0.2%
1773 45
0.2%
1957 44
0.2%
2033 73
0.3%
2182 43
0.2%
2186 45
0.2%
4049 6
 
< 0.1%
4064 15
 
0.1%
4087 75
0.3%
4170 19
 
0.1%
ValueCountFrequency (%)
274581 30
 
0.1%
208931 19
 
0.1%
188163 17
 
0.1%
177361 15
 
0.1%
158085 48
 
0.2%
108893 19
 
0.1%
10281 76
0.3%
10278 19
 
0.1%
10269 136
0.6%
10268 38
 
0.2%

home_team_goal
Real number (ℝ)

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.5518728
Minimum0
Maximum10
Zeros5391
Zeros (%)22.6%
Negative0
Negative (%)0.0%
Memory size373.4 KiB
2023-01-31T03:52:41.030248image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q32
95-th percentile4
Maximum10
Range10
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.3021647
Coefficient of variation (CV)0.83909244
Kurtosis1.1054055
Mean1.5518728
Median Absolute Deviation (MAD)1
Skewness0.93409359
Sum37082
Variance1.6956329
MonotonicityNot monotonic
2023-01-31T03:52:41.089466image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
1 7719
32.3%
2 5817
24.3%
0 5391
22.6%
3 3051
 
12.8%
4 1287
 
5.4%
5 427
 
1.8%
6 151
 
0.6%
7 37
 
0.2%
8 9
 
< 0.1%
9 4
 
< 0.1%
ValueCountFrequency (%)
0 5391
22.6%
1 7719
32.3%
2 5817
24.3%
3 3051
 
12.8%
4 1287
 
5.4%
5 427
 
1.8%
6 151
 
0.6%
7 37
 
0.2%
8 9
 
< 0.1%
9 4
 
< 0.1%
ValueCountFrequency (%)
10 2
 
< 0.1%
9 4
 
< 0.1%
8 9
 
< 0.1%
7 37
 
0.2%
6 151
 
0.6%
5 427
 
1.8%
4 1287
 
5.4%
3 3051
 
12.8%
2 5817
24.3%
1 7719
32.3%

away_team_goal
Real number (ℝ)

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1707889
Minimum0
Maximum9
Zeros7898
Zeros (%)33.1%
Negative0
Negative (%)0.0%
Memory size373.4 KiB
2023-01-31T03:52:41.147945image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile3
Maximum9
Range9
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.1457012
Coefficient of variation (CV)0.97857199
Kurtosis1.4469349
Mean1.1707889
Median Absolute Deviation (MAD)1
Skewness1.0944403
Sum27976
Variance1.3126312
MonotonicityNot monotonic
2023-01-31T03:52:41.201038image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1 8290
34.7%
0 7898
33.1%
2 4760
19.9%
3 1991
 
8.3%
4 683
 
2.9%
5 199
 
0.8%
6 59
 
0.2%
7 9
 
< 0.1%
8 5
 
< 0.1%
9 1
 
< 0.1%
ValueCountFrequency (%)
0 7898
33.1%
1 8290
34.7%
2 4760
19.9%
3 1991
 
8.3%
4 683
 
2.9%
5 199
 
0.8%
6 59
 
0.2%
7 9
 
< 0.1%
8 5
 
< 0.1%
9 1
 
< 0.1%
ValueCountFrequency (%)
9 1
 
< 0.1%
8 5
 
< 0.1%
7 9
 
< 0.1%
6 59
 
0.2%
5 199
 
0.8%
4 683
 
2.9%
3 1991
 
8.3%
2 4760
19.9%
1 8290
34.7%
0 7898
33.1%

shoton_away
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct20
Distinct (%)0.1%
Missing9740
Missing (%)40.8%
Infinite0
Infinite (%)0.0%
Mean2.9217944
Minimum0
Maximum19
Zeros5893
Zeros (%)24.7%
Negative0
Negative (%)0.0%
Memory size373.4 KiB
2023-01-31T03:52:41.260948image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q35
95-th percentile9
Maximum19
Range19
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.2285906
Coefficient of variation (CV)1.1050027
Kurtosis0.37468208
Mean2.9217944
Median Absolute Deviation (MAD)2
Skewness0.95756828
Sum41358
Variance10.423797
MonotonicityNot monotonic
2023-01-31T03:52:41.336832image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
0 5893
24.7%
4 1299
 
5.4%
3 1207
 
5.1%
5 1161
 
4.9%
2 961
 
4.0%
6 917
 
3.8%
7 731
 
3.1%
8 560
 
2.3%
1 550
 
2.3%
9 339
 
1.4%
Other values (10) 537
 
2.2%
(Missing) 9740
40.8%
ValueCountFrequency (%)
0 5893
24.7%
1 550
 
2.3%
2 961
 
4.0%
3 1207
 
5.1%
4 1299
 
5.4%
5 1161
 
4.9%
6 917
 
3.8%
7 731
 
3.1%
8 560
 
2.3%
9 339
 
1.4%
ValueCountFrequency (%)
19 2
 
< 0.1%
18 6
 
< 0.1%
17 3
 
< 0.1%
16 4
 
< 0.1%
15 15
 
0.1%
14 29
 
0.1%
13 52
 
0.2%
12 86
 
0.4%
11 114
0.5%
10 226
0.9%

shoton_home
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct26
Distinct (%)0.2%
Missing9740
Missing (%)40.8%
Infinite0
Infinite (%)0.0%
Mean3.6830802
Minimum0
Maximum26
Zeros5784
Zeros (%)24.2%
Negative0
Negative (%)0.0%
Memory size373.4 KiB
2023-01-31T03:52:41.410357image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3
Q36
95-th percentile11
Maximum26
Range26
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.9238936
Coefficient of variation (CV)1.0653837
Kurtosis0.22809631
Mean3.6830802
Median Absolute Deviation (MAD)3
Skewness0.86302017
Sum52134
Variance15.396941
MonotonicityNot monotonic
2023-01-31T03:52:41.482390image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
0 5784
24.2%
4 1076
 
4.5%
5 1076
 
4.5%
6 1021
 
4.3%
7 965
 
4.0%
3 873
 
3.7%
8 752
 
3.1%
2 578
 
2.4%
9 551
 
2.3%
10 414
 
1.7%
Other values (16) 1065
 
4.5%
(Missing) 9740
40.8%
ValueCountFrequency (%)
0 5784
24.2%
1 249
 
1.0%
2 578
 
2.4%
3 873
 
3.7%
4 1076
 
4.5%
5 1076
 
4.5%
6 1021
 
4.3%
7 965
 
4.0%
8 752
 
3.1%
9 551
 
2.3%
ValueCountFrequency (%)
26 1
 
< 0.1%
24 1
 
< 0.1%
23 1
 
< 0.1%
22 2
 
< 0.1%
21 5
 
< 0.1%
20 4
 
< 0.1%
19 7
 
< 0.1%
18 10
 
< 0.1%
17 25
0.1%
16 33
0.1%

homewin
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size373.4 KiB
1
10936 
-1
6891 
0
6068 

Length

Max length2
Median length1
Mean length1.2883867
Min length1

Characters and Unicode

Total characters30786
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row-1
5th row1

Common Values

ValueCountFrequency (%)
1 10936
45.8%
-1 6891
28.8%
0 6068
25.4%

Length

2023-01-31T03:52:41.557893image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-31T03:52:41.638269image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
1 17827
74.6%
0 6068
 
25.4%

Most occurring characters

ValueCountFrequency (%)
1 17827
57.9%
- 6891
 
22.4%
0 6068
 
19.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 23895
77.6%
Dash Punctuation 6891
 
22.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 17827
74.6%
0 6068
 
25.4%
Dash Punctuation
ValueCountFrequency (%)
- 6891
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 30786
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 17827
57.9%
- 6891
 
22.4%
0 6068
 
19.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 30786
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 17827
57.9%
- 6891
 
22.4%
0 6068
 
19.7%

shotoff_away
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct20
Distinct (%)0.1%
Missing9740
Missing (%)40.8%
Infinite0
Infinite (%)0.0%
Mean2.9788061
Minimum0
Maximum19
Zeros5859
Zeros (%)24.5%
Negative0
Negative (%)0.0%
Memory size373.4 KiB
2023-01-31T03:52:41.696315image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q35
95-th percentile9
Maximum19
Range19
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.1777634
Coefficient of variation (CV)1.066791
Kurtosis-0.059128958
Mean2.9788061
Median Absolute Deviation (MAD)2
Skewness0.81250721
Sum42165
Variance10.09818
MonotonicityNot monotonic
2023-01-31T03:52:41.769289image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
0 5859
24.5%
4 1323
 
5.5%
5 1253
 
5.2%
3 1211
 
5.1%
6 1081
 
4.5%
2 874
 
3.7%
7 764
 
3.2%
8 594
 
2.5%
1 396
 
1.7%
9 316
 
1.3%
Other values (10) 484
 
2.0%
(Missing) 9740
40.8%
ValueCountFrequency (%)
0 5859
24.5%
1 396
 
1.7%
2 874
 
3.7%
3 1211
 
5.1%
4 1323
 
5.5%
5 1253
 
5.2%
6 1081
 
4.5%
7 764
 
3.2%
8 594
 
2.5%
9 316
 
1.3%
ValueCountFrequency (%)
19 1
 
< 0.1%
18 1
 
< 0.1%
17 2
 
< 0.1%
16 1
 
< 0.1%
15 15
 
0.1%
14 18
 
0.1%
13 36
 
0.2%
12 64
 
0.3%
11 141
0.6%
10 205
0.9%

shotoff_home
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct22
Distinct (%)0.2%
Missing9740
Missing (%)40.8%
Infinite0
Infinite (%)0.0%
Mean3.7335217
Minimum0
Maximum21
Zeros5760
Zeros (%)24.1%
Negative0
Negative (%)0.0%
Memory size373.4 KiB
2023-01-31T03:52:41.843827image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3
Q37
95-th percentile11
Maximum21
Range21
Interquartile range (IQR)7

Descriptive statistics

Standard deviation3.8533676
Coefficient of variation (CV)1.0320999
Kurtosis-0.34156264
Mean3.7335217
Median Absolute Deviation (MAD)3
Skewness0.69853206
Sum52848
Variance14.848442
MonotonicityNot monotonic
2023-01-31T03:52:41.907940image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
0 5760
24.1%
6 1172
 
4.9%
5 1103
 
4.6%
4 1054
 
4.4%
7 944
 
4.0%
8 804
 
3.4%
3 802
 
3.4%
9 618
 
2.6%
2 516
 
2.2%
10 450
 
1.9%
Other values (12) 932
 
3.9%
(Missing) 9740
40.8%
ValueCountFrequency (%)
0 5760
24.1%
1 178
 
0.7%
2 516
 
2.2%
3 802
 
3.4%
4 1054
 
4.4%
5 1103
 
4.6%
6 1172
 
4.9%
7 944
 
4.0%
8 804
 
3.4%
9 618
 
2.6%
ValueCountFrequency (%)
21 1
 
< 0.1%
20 3
 
< 0.1%
19 3
 
< 0.1%
18 8
 
< 0.1%
17 12
 
0.1%
16 25
 
0.1%
15 37
 
0.2%
14 56
 
0.2%
13 125
0.5%
12 193
0.8%

foulcommit_home
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct32
Distinct (%)0.2%
Missing9740
Missing (%)40.8%
Infinite0
Infinite (%)0.0%
Mean7.5424232
Minimum0
Maximum32
Zeros5712
Zeros (%)23.9%
Negative0
Negative (%)0.0%
Memory size373.4 KiB
2023-01-31T03:52:41.979089image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median8
Q313
95-th percentile19
Maximum32
Range32
Interquartile range (IQR)13

Descriptive statistics

Standard deviation7.0612056
Coefficient of variation (CV)0.93619854
Kurtosis-1.1550249
Mean7.5424232
Median Absolute Deviation (MAD)8
Skewness0.28603457
Sum106763
Variance49.860624
MonotonicityNot monotonic
2023-01-31T03:52:42.051786image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
0 5712
23.9%
12 808
 
3.4%
13 762
 
3.2%
11 741
 
3.1%
14 695
 
2.9%
10 678
 
2.8%
9 645
 
2.7%
15 623
 
2.6%
16 521
 
2.2%
8 495
 
2.1%
Other values (22) 2475
 
10.4%
(Missing) 9740
40.8%
ValueCountFrequency (%)
0 5712
23.9%
1 2
 
< 0.1%
2 15
 
0.1%
3 33
 
0.1%
4 78
 
0.3%
5 157
 
0.7%
6 294
 
1.2%
7 384
 
1.6%
8 495
 
2.1%
9 645
 
2.7%
ValueCountFrequency (%)
32 1
 
< 0.1%
30 1
 
< 0.1%
29 6
 
< 0.1%
28 2
 
< 0.1%
27 9
 
< 0.1%
26 24
 
0.1%
25 25
 
0.1%
24 45
0.2%
23 69
0.3%
22 89
0.4%

foulcommit_away
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct32
Distinct (%)0.2%
Missing9740
Missing (%)40.8%
Infinite0
Infinite (%)0.0%
Mean7.876722
Minimum0
Maximum32
Zeros5711
Zeros (%)23.9%
Negative0
Negative (%)0.0%
Memory size373.4 KiB
2023-01-31T03:52:42.128034image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median9
Q314
95-th percentile20
Maximum32
Range32
Interquartile range (IQR)14

Descriptive statistics

Standard deviation7.3314217
Coefficient of variation (CV)0.93077066
Kurtosis-1.1897847
Mean7.876722
Median Absolute Deviation (MAD)9
Skewness0.25920391
Sum111495
Variance53.749744
MonotonicityNot monotonic
2023-01-31T03:52:42.219485image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
0 5711
23.9%
12 777
 
3.3%
13 751
 
3.1%
11 717
 
3.0%
15 694
 
2.9%
14 693
 
2.9%
10 674
 
2.8%
16 579
 
2.4%
9 574
 
2.4%
8 443
 
1.9%
Other values (22) 2542
 
10.6%
(Missing) 9740
40.8%
ValueCountFrequency (%)
0 5711
23.9%
1 9
 
< 0.1%
2 9
 
< 0.1%
3 34
 
0.1%
4 46
 
0.2%
5 138
 
0.6%
6 195
 
0.8%
7 299
 
1.3%
8 443
 
1.9%
9 574
 
2.4%
ValueCountFrequency (%)
32 1
 
< 0.1%
30 3
 
< 0.1%
29 11
 
< 0.1%
28 9
 
< 0.1%
27 16
 
0.1%
26 26
 
0.1%
25 42
 
0.2%
24 49
0.2%
23 80
0.3%
22 119
0.5%

card_home
Real number (ℝ)

MISSING  ZEROS 

Distinct11
Distinct (%)0.1%
Missing9740
Missing (%)40.8%
Infinite0
Infinite (%)0.0%
Mean1.9865772
Minimum0
Maximum10
Zeros2010
Zeros (%)8.4%
Negative0
Negative (%)0.0%
Memory size373.4 KiB
2023-01-31T03:52:42.316421image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q33
95-th percentile5
Maximum10
Range10
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.4217732
Coefficient of variation (CV)0.71568991
Kurtosis0.4959966
Mean1.9865772
Median Absolute Deviation (MAD)1
Skewness0.70680794
Sum28120
Variance2.0214391
MonotonicityNot monotonic
2023-01-31T03:52:42.378895image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
1 3803
 
15.9%
2 3784
 
15.8%
3 2547
 
10.7%
0 2010
 
8.4%
4 1265
 
5.3%
5 511
 
2.1%
6 175
 
0.7%
7 42
 
0.2%
8 14
 
0.1%
9 3
 
< 0.1%
(Missing) 9740
40.8%
ValueCountFrequency (%)
0 2010
8.4%
1 3803
15.9%
2 3784
15.8%
3 2547
10.7%
4 1265
 
5.3%
5 511
 
2.1%
6 175
 
0.7%
7 42
 
0.2%
8 14
 
0.1%
9 3
 
< 0.1%
ValueCountFrequency (%)
10 1
 
< 0.1%
9 3
 
< 0.1%
8 14
 
0.1%
7 42
 
0.2%
6 175
 
0.7%
5 511
 
2.1%
4 1265
 
5.3%
3 2547
10.7%
2 3784
15.8%
1 3803
15.9%

card_away
Real number (ℝ)

MISSING  ZEROS 

Distinct13
Distinct (%)0.1%
Missing9740
Missing (%)40.8%
Infinite0
Infinite (%)0.0%
Mean2.3569763
Minimum0
Maximum12
Zeros1295
Zeros (%)5.4%
Negative0
Negative (%)0.0%
Memory size373.4 KiB
2023-01-31T03:52:42.440645image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q33
95-th percentile5
Maximum12
Range12
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.5011935
Coefficient of variation (CV)0.63691497
Kurtosis0.50225296
Mean2.3569763
Median Absolute Deviation (MAD)1
Skewness0.6181546
Sum33363
Variance2.2535819
MonotonicityNot monotonic
2023-01-31T03:52:42.507813image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
2 3803
 
15.9%
1 3081
 
12.9%
3 3007
 
12.6%
4 1764
 
7.4%
0 1295
 
5.4%
5 803
 
3.4%
6 285
 
1.2%
7 82
 
0.3%
8 21
 
0.1%
9 11
 
< 0.1%
Other values (3) 3
 
< 0.1%
(Missing) 9740
40.8%
ValueCountFrequency (%)
0 1295
 
5.4%
1 3081
12.9%
2 3803
15.9%
3 3007
12.6%
4 1764
7.4%
5 803
 
3.4%
6 285
 
1.2%
7 82
 
0.3%
8 21
 
0.1%
9 11
 
< 0.1%
ValueCountFrequency (%)
12 1
 
< 0.1%
11 1
 
< 0.1%
10 1
 
< 0.1%
9 11
 
< 0.1%
8 21
 
0.1%
7 82
 
0.3%
6 285
 
1.2%
5 803
 
3.4%
4 1764
7.4%
3 3007
12.6%

cross_home
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct61
Distinct (%)0.4%
Missing9740
Missing (%)40.8%
Infinite0
Infinite (%)0.0%
Mean11.258001
Minimum0
Maximum72
Zeros5713
Zeros (%)23.9%
Negative0
Negative (%)0.0%
Memory size373.4 KiB
2023-01-31T03:52:42.588488image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median11
Q319
95-th percentile31
Maximum72
Range72
Interquartile range (IQR)19

Descriptive statistics

Standard deviation11.215558
Coefficient of variation (CV)0.99622998
Kurtosis-0.33001053
Mean11.258001
Median Absolute Deviation (MAD)11
Skewness0.62187079
Sum159357
Variance125.78874
MonotonicityNot monotonic
2023-01-31T03:52:42.675859image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5713
23.9%
15 479
 
2.0%
17 458
 
1.9%
14 458
 
1.9%
16 433
 
1.8%
19 419
 
1.8%
13 419
 
1.8%
18 404
 
1.7%
21 368
 
1.5%
12 360
 
1.5%
Other values (51) 4644
19.4%
(Missing) 9740
40.8%
ValueCountFrequency (%)
0 5713
23.9%
1 3
 
< 0.1%
2 12
 
0.1%
3 29
 
0.1%
4 48
 
0.2%
5 85
 
0.4%
6 92
 
0.4%
7 163
 
0.7%
8 195
 
0.8%
9 253
 
1.1%
ValueCountFrequency (%)
72 1
 
< 0.1%
62 1
 
< 0.1%
61 2
< 0.1%
59 1
 
< 0.1%
56 1
 
< 0.1%
55 2
< 0.1%
54 4
< 0.1%
53 2
< 0.1%
52 4
< 0.1%
51 2
< 0.1%

cross_away
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct54
Distinct (%)0.4%
Missing9740
Missing (%)40.8%
Infinite0
Infinite (%)0.0%
Mean8.8007065
Minimum0
Maximum59
Zeros5716
Zeros (%)23.9%
Negative0
Negative (%)0.0%
Memory size373.4 KiB
2023-01-31T03:52:42.771383image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median8
Q315
95-th percentile25
Maximum59
Range59
Interquartile range (IQR)15

Descriptive statistics

Standard deviation9.0574478
Coefficient of variation (CV)1.0291728
Kurtosis0.067566668
Mean8.8007065
Median Absolute Deviation (MAD)8
Skewness0.76619198
Sum124574
Variance82.03736
MonotonicityNot monotonic
2023-01-31T03:52:42.859584image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5716
23.9%
13 527
 
2.2%
12 526
 
2.2%
14 491
 
2.1%
9 488
 
2.0%
11 484
 
2.0%
10 476
 
2.0%
15 457
 
1.9%
16 448
 
1.9%
8 425
 
1.8%
Other values (44) 4117
17.2%
(Missing) 9740
40.8%
ValueCountFrequency (%)
0 5716
23.9%
1 20
 
0.1%
2 36
 
0.2%
3 102
 
0.4%
4 142
 
0.6%
5 219
 
0.9%
6 274
 
1.1%
7 352
 
1.5%
8 425
 
1.8%
9 488
 
2.0%
ValueCountFrequency (%)
59 1
 
< 0.1%
55 2
< 0.1%
54 1
 
< 0.1%
51 1
 
< 0.1%
50 1
 
< 0.1%
48 1
 
< 0.1%
47 2
< 0.1%
46 4
< 0.1%
45 2
< 0.1%
44 4
< 0.1%

possession_home
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct558
Distinct (%)6.6%
Missing15496
Missing (%)64.9%
Infinite0
Infinite (%)0.0%
Mean51.798543
Minimum19.5
Maximum81.125
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size373.4 KiB
2023-01-31T03:52:42.951869image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum19.5
5-th percentile35.75
Q145.75
median52
Q358
95-th percentile67.025
Maximum81.125
Range61.625
Interquartile range (IQR)12.25

Descriptive statistics

Standard deviation9.3976192
Coefficient of variation (CV)0.18142633
Kurtosis0.036166007
Mean51.798543
Median Absolute Deviation (MAD)6
Skewness-0.10121492
Sum435055.96
Variance88.315248
MonotonicityNot monotonic
2023-01-31T03:52:43.037242image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
52 116
 
0.5%
51 107
 
0.4%
53 101
 
0.4%
56 98
 
0.4%
54 97
 
0.4%
51.25 96
 
0.4%
48 94
 
0.4%
49 93
 
0.4%
54.5 93
 
0.4%
54.25 90
 
0.4%
Other values (548) 7414
31.0%
(Missing) 15496
64.9%
ValueCountFrequency (%)
19.5 1
 
< 0.1%
21.25 1
 
< 0.1%
21.5 1
 
< 0.1%
21.75 1
 
< 0.1%
22 2
< 0.1%
22.25 1
 
< 0.1%
22.5 4
< 0.1%
22.75 1
 
< 0.1%
23 1
 
< 0.1%
23.25 1
 
< 0.1%
ValueCountFrequency (%)
81.125 1
 
< 0.1%
81 1
 
< 0.1%
80.25 1
 
< 0.1%
79.75 3
< 0.1%
79 1
 
< 0.1%
78.66666667 1
 
< 0.1%
78.5 3
< 0.1%
78 2
< 0.1%
77.75 2
< 0.1%
77.66666667 1
 
< 0.1%

possession_away
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct558
Distinct (%)6.6%
Missing15496
Missing (%)64.9%
Infinite0
Infinite (%)0.0%
Mean48.201457
Minimum18.875
Maximum80.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size373.4 KiB
2023-01-31T03:52:43.128744image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum18.875
5-th percentile32.975
Q142
median48
Q354.25
95-th percentile64.25
Maximum80.5
Range61.625
Interquartile range (IQR)12.25

Descriptive statistics

Standard deviation9.3976192
Coefficient of variation (CV)0.19496546
Kurtosis0.036166007
Mean48.201457
Median Absolute Deviation (MAD)6
Skewness0.10121492
Sum404844.04
Variance88.315248
MonotonicityNot monotonic
2023-01-31T03:52:43.211820image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
48 116
 
0.5%
49 107
 
0.4%
47 101
 
0.4%
44 98
 
0.4%
46 97
 
0.4%
48.75 96
 
0.4%
52 94
 
0.4%
51 93
 
0.4%
45.5 93
 
0.4%
45.75 90
 
0.4%
Other values (548) 7414
31.0%
(Missing) 15496
64.9%
ValueCountFrequency (%)
18.875 1
 
< 0.1%
19 1
 
< 0.1%
19.75 1
 
< 0.1%
20.25 3
< 0.1%
21 1
 
< 0.1%
21.33333333 1
 
< 0.1%
21.5 3
< 0.1%
22 2
< 0.1%
22.25 2
< 0.1%
22.33333333 1
 
< 0.1%
ValueCountFrequency (%)
80.5 1
 
< 0.1%
78.75 1
 
< 0.1%
78.5 1
 
< 0.1%
78.25 1
 
< 0.1%
78 2
< 0.1%
77.75 1
 
< 0.1%
77.5 4
< 0.1%
77.25 1
 
< 0.1%
77 1
 
< 0.1%
76.75 1
 
< 0.1%

home_formation
Real number (ℝ)

Distinct27
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3624.1269
Minimum163
Maximum41221
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size373.4 KiB
2023-01-31T03:52:43.292786image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum163
5-th percentile433
Q1442
median451
Q34231
95-th percentile4411
Maximum41221
Range41058
Interquartile range (IQR)3789

Descriptive statistics

Standard deviation7778.1591
Coefficient of variation (CV)2.146216
Kurtosis18.209209
Mean3624.1269
Median Absolute Deviation (MAD)99
Skewness4.3479834
Sum86598513
Variance60499758
MonotonicityNot monotonic
2023-01-31T03:52:43.360370image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
4231 6381
26.7%
442 6320
26.4%
433 4332
18.1%
451 931
 
3.9%
4411 876
 
3.7%
352 682
 
2.9%
4312 680
 
2.8%
4141 627
 
2.6%
41212 557
 
2.3%
4222 548
 
2.3%
Other values (17) 1961
 
8.2%
ValueCountFrequency (%)
163 1
 
< 0.1%
343 283
 
1.2%
352 682
 
2.9%
433 4332
18.1%
442 6320
26.4%
451 931
 
3.9%
532 186
 
0.8%
541 69
 
0.3%
1333 1
 
< 0.1%
3232 13
 
0.1%
ValueCountFrequency (%)
41221 371
 
1.6%
41212 557
 
2.3%
14221 1
 
< 0.1%
5311 2
 
< 0.1%
4411 876
 
3.7%
4321 239
 
1.0%
4312 680
 
2.8%
4231 6381
26.7%
4222 548
 
2.3%
4213 34
 
0.1%

away_formation
Real number (ℝ)

Distinct28
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3549.5288
Minimum343
Maximum133111
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size373.4 KiB
2023-01-31T03:52:43.430893image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum343
5-th percentile433
Q1442
median451
Q34231
95-th percentile4411
Maximum133111
Range132768
Interquartile range (IQR)3789

Descriptive statistics

Standard deviation7625.9963
Coefficient of variation (CV)2.1484531
Kurtosis22.327347
Mean3549.5288
Median Absolute Deviation (MAD)99
Skewness4.5886717
Sum84815991
Variance58155819
MonotonicityNot monotonic
2023-01-31T03:52:43.509443image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
4231 6398
26.8%
442 5970
25.0%
433 4320
18.1%
451 1212
 
5.1%
4411 845
 
3.5%
4141 705
 
3.0%
352 692
 
2.9%
4312 612
 
2.6%
4222 541
 
2.3%
41212 510
 
2.1%
Other values (18) 2090
 
8.7%
ValueCountFrequency (%)
343 281
 
1.2%
352 692
 
2.9%
433 4320
18.1%
442 5970
25.0%
451 1212
 
5.1%
532 191
 
0.8%
541 135
 
0.6%
3232 16
 
0.1%
3331 13
 
0.1%
3412 134
 
0.6%
ValueCountFrequency (%)
133111 1
 
< 0.1%
41221 365
1.5%
41212 510
2.1%
33211 1
 
< 0.1%
32311 1
 
< 0.1%
31312 1
 
< 0.1%
5311 2
 
< 0.1%
4411 845
3.5%
4321 262
 
1.1%
4312 612
2.6%

winning_team
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size373.4 KiB
1
10936 
3
6891 
2
6068 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23895
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row1
3rd row1
4th row3
5th row1

Common Values

ValueCountFrequency (%)
1 10936
45.8%
3 6891
28.8%
2 6068
25.4%

Length

2023-01-31T03:52:43.597513image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-31T03:52:43.667235image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
1 10936
45.8%
3 6891
28.8%
2 6068
25.4%

Most occurring characters

ValueCountFrequency (%)
1 10936
45.8%
3 6891
28.8%
2 6068
25.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 23895
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 10936
45.8%
3 6891
28.8%
2 6068
25.4%

Most occurring scripts

ValueCountFrequency (%)
Common 23895
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 10936
45.8%
3 6891
28.8%
2 6068
25.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23895
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 10936
45.8%
3 6891
28.8%
2 6068
25.4%

home_team_result
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size373.4 KiB
Win
10936 
Loss
6891 
Draw
6068 

Length

Max length4
Median length4
Mean length3.542331
Min length3

Characters and Unicode

Total characters84644
Distinct characters10
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDraw
2nd rowWin
3rd rowWin
4th rowLoss
5th rowWin

Common Values

ValueCountFrequency (%)
Win 10936
45.8%
Loss 6891
28.8%
Draw 6068
25.4%

Length

2023-01-31T03:52:43.728363image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-31T03:52:43.813027image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
win 10936
45.8%
loss 6891
28.8%
draw 6068
25.4%

Most occurring characters

ValueCountFrequency (%)
s 13782
16.3%
W 10936
12.9%
i 10936
12.9%
n 10936
12.9%
L 6891
8.1%
o 6891
8.1%
D 6068
7.2%
r 6068
7.2%
a 6068
7.2%
w 6068
7.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 60749
71.8%
Uppercase Letter 23895
 
28.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s 13782
22.7%
i 10936
18.0%
n 10936
18.0%
o 6891
11.3%
r 6068
10.0%
a 6068
10.0%
w 6068
10.0%
Uppercase Letter
ValueCountFrequency (%)
W 10936
45.8%
L 6891
28.8%
D 6068
25.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 84644
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
s 13782
16.3%
W 10936
12.9%
i 10936
12.9%
n 10936
12.9%
L 6891
8.1%
o 6891
8.1%
D 6068
7.2%
r 6068
7.2%
a 6068
7.2%
w 6068
7.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 84644
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
s 13782
16.3%
W 10936
12.9%
i 10936
12.9%
n 10936
12.9%
L 6891
8.1%
o 6891
8.1%
D 6068
7.2%
r 6068
7.2%
a 6068
7.2%
w 6068
7.2%

away_team_result
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size373.4 KiB
Loss
10936 
Win
6891 
Draw
6068 

Length

Max length4
Median length4
Mean length3.7116133
Min length3

Characters and Unicode

Total characters88689
Distinct characters10
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDraw
2nd rowLoss
3rd rowLoss
4th rowWin
5th rowLoss

Common Values

ValueCountFrequency (%)
Loss 10936
45.8%
Win 6891
28.8%
Draw 6068
25.4%

Length

2023-01-31T03:52:43.876603image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-31T03:52:43.950067image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
loss 10936
45.8%
win 6891
28.8%
draw 6068
25.4%

Most occurring characters

ValueCountFrequency (%)
s 21872
24.7%
L 10936
12.3%
o 10936
12.3%
W 6891
 
7.8%
i 6891
 
7.8%
n 6891
 
7.8%
D 6068
 
6.8%
r 6068
 
6.8%
a 6068
 
6.8%
w 6068
 
6.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 64794
73.1%
Uppercase Letter 23895
 
26.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s 21872
33.8%
o 10936
16.9%
i 6891
 
10.6%
n 6891
 
10.6%
r 6068
 
9.4%
a 6068
 
9.4%
w 6068
 
9.4%
Uppercase Letter
ValueCountFrequency (%)
L 10936
45.8%
W 6891
28.8%
D 6068
25.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 88689
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
s 21872
24.7%
L 10936
12.3%
o 10936
12.3%
W 6891
 
7.8%
i 6891
 
7.8%
n 6891
 
7.8%
D 6068
 
6.8%
r 6068
 
6.8%
a 6068
 
6.8%
w 6068
 
6.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 88689
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
s 21872
24.7%
L 10936
12.3%
o 10936
12.3%
W 6891
 
7.8%
i 6891
 
7.8%
n 6891
 
7.8%
D 6068
 
6.8%
r 6068
 
6.8%
a 6068
 
6.8%
w 6068
 
6.8%
Distinct3
Distinct (%)< 0.1%
Missing698
Missing (%)2.9%
Memory size373.4 KiB
Win
10584 
Loss
6638 
Draw
5975 

Length

Max length4
Median length4
Mean length3.5437341
Min length3

Characters and Unicode

Total characters82204
Distinct characters10
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDraw
2nd rowWin
3rd rowWin
4th rowDraw
5th rowWin

Common Values

ValueCountFrequency (%)
Win 10584
44.3%
Loss 6638
27.8%
Draw 5975
25.0%
(Missing) 698
 
2.9%

Length

2023-01-31T03:52:44.011587image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-31T03:52:44.086643image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
win 10584
45.6%
loss 6638
28.6%
draw 5975
25.8%

Most occurring characters

ValueCountFrequency (%)
s 13276
16.2%
W 10584
12.9%
i 10584
12.9%
n 10584
12.9%
L 6638
8.1%
o 6638
8.1%
D 5975
7.3%
r 5975
7.3%
a 5975
7.3%
w 5975
7.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 59007
71.8%
Uppercase Letter 23197
 
28.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s 13276
22.5%
i 10584
17.9%
n 10584
17.9%
o 6638
11.2%
r 5975
10.1%
a 5975
10.1%
w 5975
10.1%
Uppercase Letter
ValueCountFrequency (%)
W 10584
45.6%
L 6638
28.6%
D 5975
25.8%

Most occurring scripts

ValueCountFrequency (%)
Latin 82204
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
s 13276
16.2%
W 10584
12.9%
i 10584
12.9%
n 10584
12.9%
L 6638
8.1%
o 6638
8.1%
D 5975
7.3%
r 5975
7.3%
a 5975
7.3%
w 5975
7.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 82204
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
s 13276
16.2%
W 10584
12.9%
i 10584
12.9%
n 10584
12.9%
L 6638
8.1%
o 6638
8.1%
D 5975
7.3%
r 5975
7.3%
a 5975
7.3%
w 5975
7.3%
Distinct3
Distinct (%)< 0.1%
Missing702
Missing (%)2.9%
Memory size373.4 KiB
Loss
10612 
Win
6683 
Draw
5898 

Length

Max length4
Median length4
Mean length3.7118527
Min length3

Characters and Unicode

Total characters86089
Distinct characters10
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLoss
2nd rowDraw
3rd rowWin
4th rowLoss
5th rowLoss

Common Values

ValueCountFrequency (%)
Loss 10612
44.4%
Win 6683
28.0%
Draw 5898
24.7%
(Missing) 702
 
2.9%

Length

2023-01-31T03:52:44.151792image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-31T03:52:44.226330image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
loss 10612
45.8%
win 6683
28.8%
draw 5898
25.4%

Most occurring characters

ValueCountFrequency (%)
s 21224
24.7%
L 10612
12.3%
o 10612
12.3%
W 6683
 
7.8%
i 6683
 
7.8%
n 6683
 
7.8%
D 5898
 
6.9%
r 5898
 
6.9%
a 5898
 
6.9%
w 5898
 
6.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 62896
73.1%
Uppercase Letter 23193
 
26.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s 21224
33.7%
o 10612
16.9%
i 6683
 
10.6%
n 6683
 
10.6%
r 5898
 
9.4%
a 5898
 
9.4%
w 5898
 
9.4%
Uppercase Letter
ValueCountFrequency (%)
L 10612
45.8%
W 6683
28.8%
D 5898
25.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 86089
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
s 21224
24.7%
L 10612
12.3%
o 10612
12.3%
W 6683
 
7.8%
i 6683
 
7.8%
n 6683
 
7.8%
D 5898
 
6.9%
r 5898
 
6.9%
a 5898
 
6.9%
w 5898
 
6.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 86089
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
s 21224
24.7%
L 10612
12.3%
o 10612
12.3%
W 6683
 
7.8%
i 6683
 
7.8%
n 6683
 
7.8%
D 5898
 
6.9%
r 5898
 
6.9%
a 5898
 
6.9%
w 5898
 
6.9%

Interactions

2023-01-31T03:52:36.331231image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:51:56.971955image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:51:59.087408image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:00.879828image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:02.817077image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:04.526531image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:06.220629image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:08.077824image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:09.850459image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:11.697465image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:13.571863image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:15.358564image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:17.021248image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:18.935337image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:21.473471image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:24.184177image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:26.564780image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:28.314539image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:30.367423image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:32.132756image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:33.833818image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:36.456820image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:51:57.084657image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:51:59.172694image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:00.964105image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:02.912629image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:04.612967image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:06.321461image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:08.159022image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:09.936749image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:11.777111image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:13.652510image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:15.434459image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:17.100320image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:19.015749image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:21.597521image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:24.335010image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:26.644626image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:28.394329image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:30.460028image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:32.222493image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:33.965765image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:36.544786image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:51:57.150069image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:51:59.228614image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:01.025841image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:02.969729image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:04.670444image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:06.547689image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:08.219451image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:09.997592image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:12.032864image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:13.711231image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:15.486667image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:17.153323image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:19.071102image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:21.682447image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:24.444936image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:26.700989image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:28.450982image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:30.517096image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
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2023-01-31T03:52:14.510731image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:16.189685image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:17.889039image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:20.048454image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:22.955813image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:25.769475image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:27.471412image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:29.573448image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:31.281868image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:33.032244image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:35.235094image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:37.818104image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:51:58.064045image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:00.153865image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:02.058678image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:03.784781image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:05.504708image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:07.355070image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:09.069259image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:10.897601image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:12.857507image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:14.617418image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:16.259535image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:17.963898image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:20.148798image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:23.157163image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:25.845800image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:27.550225image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:29.649026image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:31.358992image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:33.110178image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:35.347771image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:37.941441image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:51:58.139263image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:00.234432image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:02.140641image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:03.853348image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:05.577593image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:07.428806image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:09.149584image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:10.983815image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:12.933285image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:14.695506image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:16.329049image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:18.036759image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:20.277774image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:23.276080image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:25.919281image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:27.625036image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:29.720492image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:31.445905image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:33.180590image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:35.454195image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:38.057500image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:51:58.407672image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:00.316875image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:02.230912image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:03.927492image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:05.650713image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:07.503563image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:09.234598image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:11.062932image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:13.009172image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:14.776388image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:16.400331image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:18.111891image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:20.408937image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:23.387435image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:25.995604image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:27.702818image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:29.797215image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:31.522662image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:33.255215image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:35.581101image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:38.175796image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:51:58.486521image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:00.400844image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:02.312060image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:04.003187image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:05.730305image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:07.583041image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:09.312108image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:11.146890image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:13.086086image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:14.857005image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:16.533877image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:18.437846image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:20.548828image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:23.507296image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:26.075665image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:27.780195image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:29.878573image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:31.613801image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:33.331121image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:35.694772image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:38.286348image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:51:58.568964image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:00.483549image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:02.387074image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:04.087882image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:05.803239image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:07.656659image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:09.390559image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:11.265733image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:13.160442image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:14.935821image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:16.606967image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:18.513410image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:20.662797image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:23.615184image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:26.152972image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:27.893915image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:29.952138image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:31.688360image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:33.407161image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:35.803535image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:38.737848image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:51:58.656556image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:00.558702image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:02.464380image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:04.168715image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:05.877919image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:07.733672image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:09.467403image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:11.346970image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:13.239397image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:15.016744image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:16.693100image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:18.586027image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:20.870457image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:23.764214image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:26.231059image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:27.976407image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:30.029561image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:31.762186image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:33.495472image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:35.914279image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:38.875108image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:51:58.763963image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:00.650382image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:02.556039image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:04.266980image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:05.962623image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:07.819082image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:09.553572image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:11.437917image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:13.323945image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:15.107518image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:16.776521image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:18.673096image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:21.035053image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:23.866957image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:26.314249image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:28.063206image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:30.117478image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:31.845516image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:33.579764image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:36.034096image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:39.036077image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:51:58.921651image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:00.773027image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:02.687973image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:04.399373image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:06.093497image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:07.953879image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:09.712588image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:11.570721image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:13.452238image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:15.233509image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:16.902471image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:18.806994image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:21.251787image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:24.033491image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:26.440871image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:28.189825image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:30.246264image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:32.009464image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:33.704294image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T03:52:36.186792image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Correlations

2023-01-31T03:52:44.311906image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
country_idseasonstagehome_team_api_idaway_team_api_idhome_team_goalaway_team_goalshoton_awayshoton_homeshotoff_awayshotoff_homefoulcommit_homefoulcommit_awaycard_homecard_awaycross_homecross_awaypossession_homepossession_awayhome_formationaway_formationhomewinwinning_teamhome_team_resultaway_team_resulthome_1st_last_resultaway_1st_last_result
country_id1.0000.0370.010-0.022-0.0210.0080.011-0.245-0.252-0.237-0.243-0.114-0.1350.2710.244-0.272-0.2700.008-0.0080.0270.0270.0250.0250.0250.0250.0280.026
season0.0371.000-0.028-0.048-0.051-0.0020.0210.1760.1710.1470.1400.1950.1990.014-0.0110.1470.1580.006-0.006-0.240-0.2420.0100.0100.0100.0100.0140.018
stage0.010-0.0281.0000.0010.0040.0140.001-0.029-0.032-0.035-0.031-0.048-0.055-0.028-0.048-0.026-0.028-0.0020.0020.0020.0020.0160.0160.0160.0160.0180.018
home_team_api_id-0.022-0.0480.0011.0000.0460.037-0.0080.0150.0050.0150.003-0.0010.000-0.034-0.0350.0200.0120.016-0.0160.0700.0510.0200.0200.0200.0200.0060.010
away_team_api_id-0.021-0.0510.0040.0461.000-0.0090.0140.0000.0190.0050.0230.0150.016-0.027-0.0380.0200.017-0.0260.0260.0580.0660.0090.0090.0090.0090.0000.011
home_team_goal0.008-0.0020.0140.037-0.0091.000-0.049-0.0290.024-0.0300.004-0.032-0.009-0.1150.040-0.031-0.0020.214-0.214-0.0040.0060.4540.4540.4540.4540.0390.024
away_team_goal0.0110.0210.001-0.0080.014-0.0491.0000.033-0.0140.017-0.0110.017-0.0020.136-0.0310.020-0.014-0.1700.1700.002-0.0130.4850.4850.4850.4850.0240.037
shoton_away-0.2450.176-0.0290.0150.000-0.0290.0331.0000.7260.8190.7160.7720.745-0.008-0.0500.7220.838-0.3130.3130.2390.1940.0400.0400.0400.0400.0240.038
shoton_home-0.2520.171-0.0320.0050.0190.024-0.0140.7261.0000.7160.8330.7430.764-0.073-0.0200.8470.7270.325-0.3250.2230.2210.0250.0250.0250.0250.0330.017
shotoff_away-0.2370.147-0.0350.0150.005-0.0300.0170.8190.7161.0000.7240.7710.747-0.011-0.0590.7210.846-0.2880.2880.2410.2020.0280.0280.0280.0280.0290.028
shotoff_home-0.2430.140-0.0310.0030.0230.004-0.0110.7160.8330.7241.0000.7430.769-0.075-0.0090.8530.7240.303-0.3030.2300.2310.0140.0140.0140.0140.0400.024
foulcommit_home-0.1140.195-0.048-0.0010.015-0.0320.0170.7720.7430.7710.7431.0000.8360.1120.0340.7520.777-0.1620.1620.1930.1690.0370.0370.0370.0370.0350.000
foulcommit_away-0.1350.199-0.0550.0000.016-0.009-0.0020.7450.7640.7470.7690.8361.0000.0160.1170.7670.7630.078-0.0780.1920.1740.0210.0210.0210.0210.0220.023
card_home0.2710.014-0.028-0.034-0.027-0.1150.136-0.008-0.073-0.011-0.0750.1120.0161.0000.255-0.062-0.028-0.1460.146-0.029-0.0500.1050.1050.1050.1050.0360.025
card_away0.244-0.011-0.048-0.035-0.0380.040-0.031-0.050-0.020-0.059-0.0090.0340.1170.2551.000-0.019-0.0540.082-0.082-0.045-0.0330.0510.0510.0510.0510.0000.023
cross_home-0.2720.147-0.0260.0200.020-0.0310.0200.7220.8470.7210.8530.7520.767-0.062-0.0191.0000.7590.285-0.2850.2390.2380.0630.0630.0630.0630.0220.024
cross_away-0.2700.158-0.0280.0120.017-0.002-0.0140.8380.7270.8460.7240.7770.763-0.028-0.0540.7591.000-0.3230.3230.2510.2130.0520.0520.0520.0520.0290.003
possession_home0.0080.006-0.0020.016-0.0260.214-0.170-0.3130.325-0.2880.303-0.1620.078-0.1460.0820.285-0.3231.000-1.000-0.0590.0680.1620.1620.1620.1620.0520.086
possession_away-0.008-0.0060.002-0.0160.026-0.2140.1700.313-0.3250.288-0.3030.162-0.0780.146-0.082-0.2850.323-1.0001.0000.059-0.0680.1620.1620.1620.1620.0520.086
home_formation0.027-0.2400.0020.0700.058-0.0040.0020.2390.2230.2410.2300.1930.192-0.029-0.0450.2390.251-0.0590.0591.0000.4270.0430.0430.0430.0430.0280.011
away_formation0.027-0.2420.0020.0510.0660.006-0.0130.1940.2210.2020.2310.1690.174-0.050-0.0330.2380.2130.068-0.0680.4271.0000.0410.0410.0410.0410.0120.033
homewin0.0250.0100.0160.0200.0090.4540.4850.0400.0250.0280.0140.0370.0210.1050.0510.0630.0520.1620.1620.0430.0411.0001.0001.0001.0000.0370.046
winning_team0.0250.0100.0160.0200.0090.4540.4850.0400.0250.0280.0140.0370.0210.1050.0510.0630.0520.1620.1620.0430.0411.0001.0001.0001.0000.0370.046
home_team_result0.0250.0100.0160.0200.0090.4540.4850.0400.0250.0280.0140.0370.0210.1050.0510.0630.0520.1620.1620.0430.0411.0001.0001.0001.0000.0370.046
away_team_result0.0250.0100.0160.0200.0090.4540.4850.0400.0250.0280.0140.0370.0210.1050.0510.0630.0520.1620.1620.0430.0411.0001.0001.0001.0000.0370.046
home_1st_last_result0.0280.0140.0180.0060.0000.0390.0240.0240.0330.0290.0400.0350.0220.0360.0000.0220.0290.0520.0520.0280.0120.0370.0370.0370.0371.0000.004
away_1st_last_result0.0260.0180.0180.0100.0110.0240.0370.0380.0170.0280.0240.0000.0230.0250.0230.0240.0030.0860.0860.0110.0330.0460.0460.0460.0460.0041.000

Missing values

2023-01-31T03:52:39.260962image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-01-31T03:52:39.618931image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-01-31T03:52:39.927246image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

country_idseasonstagedatehome_team_api_idaway_team_api_idhome_team_goalaway_team_goalshoton_awayshoton_homehomewinshotoff_awayshotoff_homefoulcommit_homefoulcommit_awaycard_homecard_awaycross_homecross_awaypossession_homepossession_awayhome_formationaway_formationwinning_teamhome_team_resultaway_team_resulthome_1st_last_resultaway_1st_last_result
match_api_id
49301612008242009-02-289996863511NaNNaN0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN4424422DrawDrawNaNNaN
49301712008242009-02-278203998721NaNNaN1NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN4424421WinLossNaNNaN
49301812008242009-02-289986999830NaNNaN1NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN4424421WinLossNaNNaN
49302012008242009-03-019994999101NaNNaN-1NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN4424423LossWinNaNNaN
49302112008242009-02-288342999921NaNNaN1NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN4424421WinLossNaNNaN
49302212008242009-02-289993857130NaNNaN1NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN4424421WinLossNaNNaN
49302312008242009-02-287947404940NaNNaN1NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN4424421WinLossNaNNaN
49302412008242009-02-28100001000111NaNNaN0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN4424422DrawDrawNaNNaN
49302512008252009-03-089984834213NaNNaN-1NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN4424423LossWinNaNLoss
49302712008252009-03-0786351000020NaNNaN1NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN4424421WinLossDrawDraw
country_idseasonstagedatehome_team_api_idaway_team_api_idhome_team_goalaway_team_goalshoton_awayshoton_homehomewinshotoff_awayshotoff_homefoulcommit_homefoulcommit_awaycard_homecard_awaycross_homecross_awaypossession_homepossession_awayhome_formationaway_formationwinning_teamhome_team_resultaway_team_resulthome_1st_last_resultaway_1st_last_result
match_api_id
199208424558201572015-08-3099311024331NaNNaN1NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN42314421WinLossWinWin
199208524558201572015-08-3099561019232NaNNaN1NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN423142311WinLossLossWin
199208624558201582015-09-1299311019021NaNNaN1NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN423141411WinLossWinLoss
199208724558201582015-09-1210192982440NaNNaN1NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN4424331WinLossWinLoss
199208824558201582015-09-1310199995633NaNNaN0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN4121242312DrawDrawLossLoss
199208924558201582015-09-13102431019133NaNNaN0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN44242312DrawDrawWinWin
199209124558201592015-09-22101901019110NaNNaN1NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN423142311WinLossWinDraw
199209224558201592015-09-2398241019912NaNNaN-1NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN34124333LossWinWinDraw
199209324558201592015-09-2399561017920NaNNaN1NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN423142311WinLossDrawWin
199209524558201592015-09-2310192993143NaNNaN1NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN44242311WinLossWinLoss